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Found 8 papers, 3 papers with code
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images.
Segmentation of Pulmonary Opacification in Chest CT Scans of COVID-19 Patients
In this work we provide open source models for the segmentation of patterns of pulmonary opacification on chest Computed Tomography (CT) scans which have been correlated with various stages and severities of infection.
A Weakly Supervised Consistency-based Learning Method for COVID-19 Segmentation in CT Images
Thus, we propose a consistency-based (CB) loss function that encourages the output predictions to be consistent with spatial transformations of the input images.
Fully reversible neural networks for large-scale surface and sub-surface characterization via remote sensing
The large spatial/frequency scale of hyperspectral and airborne magnetic and gravitational data causes memory issues when using convolutional neural networks for (sub-) surface characterization.
Symmetric block-low-rank layers for fully reversible multilevel neural networks
Factors that limit the size of the input and output of a neural network include memory requirements for the network states/activations to compute gradients, as well as memory for the convolutional kernels or other weights.
Fluid Flow Mass Transport for Generative Networks
Generative Adversarial Networks have been shown to be powerful in generating content.
Fully Hyperbolic Convolutional Neural Networks
However, their application to problems with high dimensional input and output, such as high-resolution image and video segmentation or 3D medical imaging, has been limited by various factors.
IMEXnet: A Forward Stable Deep Neural Network
Deep convolutional neural networks have revolutionized many machine learning and computer vision tasks, however, some remaining key challenges limit their wider use.
